ADD: allow for choice of nb of parallel iterations when using LBFGS (#…

…167)

* ADD: allow for choice of nb of parallel iterations when using LBFGS

* ADD: full cLogit test

* ADD: report computation in tests

choice_learn/models/base_model.py

@@ -22,7 +22,8 @@ def __init__(
         label_smoothing=0.0,
         add_exit_choice=False,
         optimizer="lbfgs",
-        tolerance=1e-8,
+        lbfgs_tolerance=1e-8,
+        lbfgs_parallel_iterations=4,
         callbacks=None,
         lr=0.001,
         epochs=1000,
@@ -45,8 +46,10 @@ def __init__(
             List of callbacks to add to model.fit, by default None and only add History
         optimizer : str, optional
             Name of the tf.keras.optimizers to be used, by default "lbfgs"
-        tolerance : float, optional
+        lbfgs_tolerance : float, optional
             Tolerance for the L-BFGS optimizer if applied, by default 1e-8
+        lbfgs_parallel_iterations : int, optional
+            Number of parallel iterations for the L-BFGS optimizer, by default 4
         lr: float, optional
             Learning rate for the optimizer if applied, by default 0.001
         epochs: int, optional
@@ -99,7 +102,8 @@ def __init__(
 
         self.epochs = epochs
         self.batch_size = batch_size
-        self.tolerance = tolerance
+        self.lbfgs_tolerance = lbfgs_tolerance
+        self.lbfgs_parallel_iterations = lbfgs_parallel_iterations
 
         if regularization is not None:
             if np.sum(regularization_strength) <= 0:
@@ -778,9 +782,10 @@ def _fit_with_lbfgs(self, choice_dataset, sample_weight=None, verbose=0):
             value_and_gradients_function=func,
             initial_position=init_params,
             max_iterations=epochs,
-            tolerance=self.tolerance,
+            tolerance=self.lbfgs_tolerance,
             f_absolute_tolerance=-1,
             f_relative_tolerance=-1,
+            parallel_iterations=self.lbfgs_parallel_iterations,
         )
 
         # after training, the final optimized parameters are still in results.position

tests/integration_tests/models/test_conditional_logit.py

@@ -8,6 +8,7 @@
 
 def test_mode_canada_gt():
     """Tests specific config of cLogit and .evaluate()."""
+    tf.config.run_functions_eagerly(True)
     # Instantiation with the coefficients dictionnary
     coefficients = {
         "income": "item",
@@ -36,3 +37,26 @@ def test_mode_canada_gt():
     total_nll = gt_model.evaluate(canada_dataset) * len(canada_dataset)
     assert total_nll <= 1874.4, f"Got NLL: {total_nll}"
     assert total_nll >= 1874.1, f"Got NLL: {total_nll}"
+
+
+def test_mode_canada_fit():
+    """Tests specific config of cLogit and .fit()."""
+    tf.config.run_functions_eagerly(True)
+    # Instantiation with the coefficients dictionnary
+    coefficients = {
+        "income": "item",
+        "cost": "constant",
+        "freq": "constant",
+        "ovt": "constant",
+        "ivt": "item-full",
+        "intercept": "item",
+    }
+
+    canada_dataset = load_modecanada(as_frame=False, preprocessing="tutorial")
+
+    model = ConditionalLogit(coefficients=coefficients)
+    model.fit(canada_dataset, get_report=True)
+
+    total_nll = model.evaluate(canada_dataset) * len(canada_dataset)
+    assert total_nll <= 1874.4, f"Got NLL: {total_nll}"
+    assert total_nll >= 1874.1, f"Got NLL: {total_nll}"